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FacelandmarkdetectionusingCNN
Task
Theobjectiveofthistaskistopredictkeypointpositionsonfaceimages.
Usage
trackingfacesinimagesandvideo
analyzingfacialexpressions
detectingdysmorphicfacialsignsformedicaldiagnosis
biometrics/facerecognition
Method
DeepLearning
CNNregression
ComputerVision
FacelandmarkdetectionusingCNN
SomeDatasets
CUHK: MALF&MTLF
12995,20000images
5keypoints
Kaggle: FacialKeypointDetection
7049Images
96x96
15keypoints
ComputerVision
/notes/2014/12/17/using-
convolutional-neural-nets-to-detect-facial-keypoints-thutttop:r/ia/lm/#mthlaeb-.hk/projects/TCDCN.ht
FacelandmarkdetectionusingCNN
Method
CNN
Regression
Whatlossshouldweuse?
ComputerVision
.hk/projects/TCDCN.ht
ml
FacelandmarkdetectionusingCNN
Method
CNN
Regression
Whatlossshouldweuse?
Consider:
Whatlabelwehave?
Whattargetwewant?
Howtocomputeloss?
ComputerVision
.hk/projects/TCDCN.ht
ml
FacelandmarkdetectionusingCNN
Method
CNN
Regression
Howtocomputeloss?
Whatlossshouldweuse?
ComputerVision
Consider:
Whatlabelwehave?
Keypoints,
e.g.,{(x1,y1),(x2,y2)…
Whattargetwewant?
Locationsofkeypoints
.hk/projects/TCDC
N.html
FacelandmarkdetectionusingCNN
•
ComputerVision
10 E
/.hk/projects/TCDCN.html
10
GroundTruth
Loss
FacelandmarkdetectionusingCNN
•
ComputerVision
FacelandmarkdetectionusingCNN
•
ComputerVision
Schedule
FacelandmarkdetectionusingCNN
CNNregression
Faciallandmarkdetection
GenderClassificationonFaceimageusingCNN
TransferLearning
AdvancedTopics
RNN/LSTMincomputervision
Cuttingedgetechniquesindeeplearning
ClassSummary
ComputerVision
FaceGenderClassification
GenderClassificationonFaceimageusingCNN
Problem:
Estimategender(Male/Female)givenafaceimage
Data:
FaceImage
GenderInformation(2classproblem)
Database:
CUHK:MALF&MTLF
CUHK:CelebA
ComputerVision
FaceGenderClassification
GenderClassificationonFaceimageusingCNN
Method:
CNN
Whatisourtrainingdata?
Faceimage
Gender:{0,1}=>{Male,Female}
Howtoorganizethetraining?
Trainfromscratch
Usepre-trainedmodelandfine-tuning
Whatnetworkshouldweuse?
Whatlossshouldweuse?
Howtotestourmodel?
ComputerVision
FaceGenderClassification
GenderClassificationonFaceimageusingCNN
Whatisourtrainingdata?
Faceimage
Gender:{0,1}=>{Male,Female}
Howtoorganizethetraining?
Trainfromscratch
Usepre-trainedmodelandfine-tuning
Whatnetworkshouldweuse?
AlexNet,VGG,ResNet18,etc…
Whatlossshouldweuse?
Softmax
CrossEntropy
Howtotestourmodel?
ComputerVision
FaceGenderClassification
GenderClassificationonFaceimageusingCNN
Method:
Usepre-trainedmodelandfine-tuning
Idea:
FaceRecognitionmodel
+
FacewithGenderData
+
Training
=
GenderModel
ComputerVision
Fine-tuningtakesanalreadylearnedmodel,adaptsthearchitecture,andresumestrainingfromthealreadylearnedmodelweights.
FaceGenderClassification
GenderClassificationonFaceimageusingCNN
Method:
Usepre-trainedmodelandfine-tuning
Idea:
FaceRecognitionmodel
+
FacewithGenderData
+
Training
=
GenderModel
TransferLearning
ComputerVision
FaceGenderClassification
GenderClassificationonFaceimageusingCNN
Method:
Usepre-trainedmodelandfine-tuning
Pre-trainedmodel:
VGGFaceRecognitionmodel(forclassificatione.g.,1000identities)
Fine-tunethenet:
FaceGenderData
Similarnetworkarchitecture
Changelastlayer(s)tothegenderclassificationtask
Extension:
Task1
Task2
Task3
Multi-taskDCNN
ComputerVision
Schedule
FacelandmarkdetectionusingCNN
CNNregression
Faciallandmarkdetection
GenderClassificationonFaceimageusingCNN
TransferLearning
AdvancedTopics
RNN/LSTMincomputervision
Activationsfunctions
AdvancedLayers
AdvancedNetworkarchitectures
TrainingTricks
ClassSummary
ComputerVision
RNN/LSTMinComputerVision
RecurrentNeuralNetwork(RNN)
ComputerVision
Aloopallowsinformationtobepassedfromonestepofthenetworktothenext
http://colah.github.io/posts/2015-08-Understanding-
RNN/LSTMinComputerVision
RecurrentNeuralNetwork(RNN)
WhyweneedRNN?
ComputerVision
Handwave?Standup?
RNN/LSTMinComputerVision
RecurrentNeuralNetwork(RNN)
RNNProblem
LSTM
Long-ShortTermMemory
ComputerVision
StandardRNN
LSTM
RNN/LSTMinComputerVision
RecurrentNeuralNetwork(RNN)
ApplicationsinComputervision:
ObjectTracking
ActionRecognition
VideoCaptioning
Videoanalysis
Imagegeneralization
Applicationsinothermlarea:
Translation
NLP(e.g.,wordprediction)
Speechrecognition
ComputerVision
AdvancedTopics
ActivationFunction
Sigmoid,Tanh,ReLU
AdvancedActivationFunctions:
LeakyReLU
ParametricReLU
RandomizedRuLU
ELU
ComputerVision
AdvancedTopics
AdvancedLayers
DilatedConv
BNLayer
RecurrentLayer
RNN
LSTM
ComputerVision
AdvancedTopics
AdvancedLayers
DilatedConv
ComputerVision
Dilatedconvolutions“inflate”thekernelbyinsertingspacesbetweenthekernelelements.
largerreceptivefield,
efficientcomputationandlessermemoryconsumption
Poolingmakesreceptivefieldsmallerandsmaller
Up-samplingcannotrestorelostinformation
Dilatedconvhelpkeepthereceptivefiled
AdvancedTopics
BNLayer
IssuesWithTrainingDeepNeuralNetworks
InternalCovariateshift
VanishingGradient
AdvantagesofBN:
Reducesinternalcovariantshift.
t
Reducesthedependenceofgradientsonscaleoftheparametersortheirinitialvalues.
Regularizesthemodelandreducestheneedfordropout,localresponsenormalizationandotherregularizationtechniques.
Allowsuseofsaturatingnonlinearitiesandhigherlearningrates.
ComputerVision
AdvancedTopics
NetworkStructure
DenseNet
ResNext
SqueezeNet
TinyDarknet
ComputerVision
AdvancedTopics
NetworkStructure
DenseNet
ResNext
SqueezeNet
TinyDarknet
ComputerVision
AdvancedTopics
NetworkStructure
DenseNet
ResNext
SqueezeNet
TinyDarknet
ComputerVision
AdvancedTopics
NetworkStructure
DenseNet
ResNext
SqueezeNet
TinyDarknet
ComputerVision
AdvancedTopics
NetworkStructure
1x1conv
Combinemultiplechannels
Dimensionreduction
ComputerVision
1x1convWith32
56 filters 56
Eachfilter
56 hassize 56
64 1x1x64,and 32
performsa64dimdotproduct
TrainingTricks
GPU 分布式训练
Synchronous
PlacesanindividualmodelreplicaoneachGPU.SplitthebatchacrosstheGPUs.
UpdatesmodelparameterssynchronouslybywaitingforallGPUstofinishprocessingabatchofdata.
Asynchronous
ComputerVision
TrainingTricks
GPU 分布式训练
Synchronous
“lastexecutor”effect
ComputerVision
synchronoussystemshavetowaitontheslowestexecutorbeforecompletingeachiteration.
TrainingTricks
GPU 分布式训练
Asynchronous
Stalegradientproblem
ComputerVision
TrainingTricks
DataNormalization
InputData
Continuousdata:
Normalizeto[0,1]or[-1,1],ormean=0&std=1
DiscreteLabeldata: Onehotvector
E.g.,3classes[0,1,2] ➔[[1,0,0],[0,1,0],[0,0,1]]
ComputerVision
Note:Normalizationmethodintrainingantestingmustbethesame!
TrainingTricks
WeightInitialization
Principle:
Nottoolarge,Nottoosmall
Xavier
Gaussian
biasusuallysettoconstant(e.g.,0)
Etc.
ComputerVision
TrainingTricks
EpochandIteration
EpochUsually>>1
#ofIterations=#ofEpoch*data_size/batch_size
Small#ofEpoch:
Underfitting
Large#ofEpoch:
Overfitting
Howtodecide?
Earlystopping
ComputerVision
TrainingTricks
LearningRate
Oneofthemostimportantparamintraining
Toosmall:
Slow,sometimesnotconverge
Toolarge:
Noconvergence
Usuallyrange:
0.1---1e-6
Howtodecide?
Visualizetraining
TrainingfromscratchandFinetuning
ComputerVision
TrainingTricks
Activationfunction
Hiddenlayers:
ReLUandLeakyRelu
LSTM:
SigmoidandTanh
ComputerVision
OutputLayers:
Classification:Softmax
Regression:Identity
no-opactivation,usefultoimplementlinearbottleneck,returnsf(x)=x
TrainingTricks
Lossfunction:
Yournet’spurposedeterminethelossfunctionyouuse.
Forexample,
inclassificationproblem:usemulticlasscrossentropyloss.
inregressionproblem:useEuclideanloss.
ComputerVision
TrainingTricks
Regularization:
Helppreventoverfitting
L1andL2regularization
UsuallyL2,decay1e-3to1e-6
Dropout
Usually0.3or0.5
EarlyStopping
ComputerVision
TrainingTricks
BatchSize:
Toosmall:
Slowtraining
DonotutilizeGPU
Toolarge:
Overfitting(ICLR2017paper)
Usually:
16,32,128…
ComputerVision
TrainingTricks
Solver/Optimizer:
SGD
Momentum
Adam/RMSProp
ComputerVision
Schedule
FacelandmarkdetectionusingCNN
CNNregression
Faciallandmarkdetection
GenderClassificationonFaceimageusingCNN
TransferLearning
AdvancedTopics
RNN/LSTMincomputervision
Activationsfunctions
AdvancedLayers
AdvancedNetworkarchitectures
TrainingTricks
ClassSummary
ComputerVision
ComputerVision
ClassSummary
Week 1
机器的力量:将数据转化为知识
机器学习的整体概念
监督学习,非监督学习,增强学习
机器学习系统的Roadmap
Machinelearning经典算法:机器学习≠深度学习
K-meansclustering
K-NN,SVM
Regression Task
Experience
LearnedProgram
Tas
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